Overview

Dataset statistics

Number of variables40
Number of observations12167
Missing cells30055
Missing cells (%)6.2%
Duplicate rows19
Duplicate rows (%)0.2%
Total size in memory3.8 MiB
Average record size in memory328.0 B

Variable types

Categorical34
Numeric6

Alerts

Dataset has 19 (0.2%) duplicate rowsDuplicates
ocorrencia_cidade has a high cardinality: 1068 distinct values High cardinality
ocorrencia_aerodromo has a high cardinality: 516 distinct values High cardinality
ocorrencia_dia has a high cardinality: 2684 distinct values High cardinality
ocorrencia_hora has a high cardinality: 923 distinct values High cardinality
ocorrencia_localizacao has a high cardinality: 2702 distinct values High cardinality
ocorrencia_tipo has a high cardinality: 80 distinct values High cardinality
ocorrencia_tipo_categoria has a high cardinality: 80 distinct values High cardinality
aeronave_matricula has a high cardinality: 3902 distinct values High cardinality
aeronave_fabricante has a high cardinality: 234 distinct values High cardinality
aeronave_modelo has a high cardinality: 738 distinct values High cardinality
aeronave_tipo_icao has a high cardinality: 232 distinct values High cardinality
aeronave_voo_origem has a high cardinality: 678 distinct values High cardinality
aeronave_voo_destino has a high cardinality: 675 distinct values High cardinality
fator_nome has a high cardinality: 74 distinct values High cardinality
recomendacao_conteudo has a high cardinality: 1171 distinct values High cardinality
aeronave_pmd is highly correlated with aeronave_pmd_categoria and 1 other fieldsHigh correlation
aeronave_pmd_categoria is highly correlated with aeronave_pmd and 1 other fieldsHigh correlation
aeronave_assentos is highly correlated with aeronave_pmd and 1 other fieldsHigh correlation
aeronave_pmd is highly correlated with aeronave_pmd_categoria and 1 other fieldsHigh correlation
aeronave_pmd_categoria is highly correlated with aeronave_pmd and 1 other fieldsHigh correlation
aeronave_assentos is highly correlated with aeronave_pmd and 1 other fieldsHigh correlation
aeronave_pmd is highly correlated with aeronave_pmd_categoria and 1 other fieldsHigh correlation
aeronave_pmd_categoria is highly correlated with aeronave_pmd and 1 other fieldsHigh correlation
aeronave_assentos is highly correlated with aeronave_pmd and 1 other fieldsHigh correlation
fator_area is highly correlated with fator_aspecto and 2 other fieldsHigh correlation
aeronave_operador_categoria is highly correlated with divulgacao_relatorio_publicado and 1 other fieldsHigh correlation
aeronave_registro_categoria is highly correlated with aeronave_motor_tipo and 1 other fieldsHigh correlation
fator_aspecto is highly correlated with fator_area and 2 other fieldsHigh correlation
total_aeronaves_envolvidas is highly correlated with fator_nome and 3 other fieldsHigh correlation
aeronave_nivel_dano is highly correlated with divulgacao_relatorio_publicado and 1 other fieldsHigh correlation
aeronave_motor_tipo is highly correlated with aeronave_registro_categoria and 2 other fieldsHigh correlation
aeronave_tipo_operacao is highly correlated with aeronave_registro_segmentoHigh correlation
ocorrencia_saida_pista is highly correlated with taxonomia_tipo_icao and 2 other fieldsHigh correlation
divulgacao_relatorio_publicado is highly correlated with aeronave_operador_categoria and 4 other fieldsHigh correlation
fator_nome is highly correlated with fator_area and 3 other fieldsHigh correlation
aeronave_tipo_veiculo is highly correlated with aeronave_registro_categoria and 1 other fieldsHigh correlation
fator_condicionante is highly correlated with fator_area and 2 other fieldsHigh correlation
aeronave_motor_quantidade is highly correlated with aeronave_motor_tipoHigh correlation
taxonomia_tipo_icao is highly correlated with total_aeronaves_envolvidas and 4 other fieldsHigh correlation
ocorrencia_tipo_categoria is highly correlated with total_aeronaves_envolvidas and 5 other fieldsHigh correlation
ocorrencia_classificacao is highly correlated with aeronave_operador_categoria and 5 other fieldsHigh correlation
aeronave_registro_segmento is highly correlated with aeronave_tipo_operacaoHigh correlation
ocorrencia_tipo is highly correlated with total_aeronaves_envolvidas and 5 other fieldsHigh correlation
ocorrencia_classificacao is highly correlated with ocorrencia_uf and 14 other fieldsHigh correlation
ocorrencia_uf is highly correlated with ocorrencia_classificacao and 12 other fieldsHigh correlation
divulgacao_relatorio_publicado is highly correlated with total_recomendacoes and 4 other fieldsHigh correlation
total_recomendacoes is highly correlated with ocorrencia_classificacao and 12 other fieldsHigh correlation
total_aeronaves_envolvidas is highly correlated with total_recomendacoes and 4 other fieldsHigh correlation
ocorrencia_saida_pista is highly correlated with ocorrencia_tipo and 3 other fieldsHigh correlation
ocorrencia_tipo is highly correlated with ocorrencia_classificacao and 26 other fieldsHigh correlation
ocorrencia_tipo_categoria is highly correlated with ocorrencia_classificacao and 26 other fieldsHigh correlation
taxonomia_tipo_icao is highly correlated with ocorrencia_classificacao and 16 other fieldsHigh correlation
aeronave_operador_categoria is highly correlated with ocorrencia_classificacao and 5 other fieldsHigh correlation
aeronave_tipo_veiculo is highly correlated with ocorrencia_tipo and 6 other fieldsHigh correlation
aeronave_motor_tipo is highly correlated with ocorrencia_classificacao and 14 other fieldsHigh correlation
aeronave_motor_quantidade is highly correlated with ocorrencia_classificacao and 9 other fieldsHigh correlation
aeronave_pmd is highly correlated with ocorrencia_classificacao and 11 other fieldsHigh correlation
aeronave_pmd_categoria is highly correlated with ocorrencia_classificacao and 11 other fieldsHigh correlation
aeronave_assentos is highly correlated with ocorrencia_tipo and 8 other fieldsHigh correlation
aeronave_ano_fabricacao is highly correlated with ocorrencia_uf and 4 other fieldsHigh correlation
aeronave_registro_categoria is highly correlated with ocorrencia_tipo and 6 other fieldsHigh correlation
aeronave_registro_segmento is highly correlated with ocorrencia_classificacao and 17 other fieldsHigh correlation
aeronave_fase_operacao is highly correlated with ocorrencia_classificacao and 15 other fieldsHigh correlation
aeronave_tipo_operacao is highly correlated with ocorrencia_classificacao and 17 other fieldsHigh correlation
aeronave_nivel_dano is highly correlated with ocorrencia_classificacao and 8 other fieldsHigh correlation
aeronave_fatalidades_total is highly correlated with ocorrencia_uf and 7 other fieldsHigh correlation
fator_nome is highly correlated with ocorrencia_classificacao and 18 other fieldsHigh correlation
fator_aspecto is highly correlated with ocorrencia_tipo and 4 other fieldsHigh correlation
fator_condicionante is highly correlated with ocorrencia_tipo and 5 other fieldsHigh correlation
fator_area is highly correlated with ocorrencia_tipo and 4 other fieldsHigh correlation
recomendacao_status is highly correlated with ocorrencia_tipo and 2 other fieldsHigh correlation
ocorrencia_localizacao has 983 (8.1%) missing values Missing
aeronave_assentos has 241 (2.0%) missing values Missing
aeronave_ano_fabricacao has 246 (2.0%) missing values Missing
fator_nome has 4432 (36.4%) missing values Missing
fator_aspecto has 4432 (36.4%) missing values Missing
fator_condicionante has 4432 (36.4%) missing values Missing
fator_area has 4432 (36.4%) missing values Missing
recomendacao_conteudo has 5424 (44.6%) missing values Missing
recomendacao_status has 5422 (44.6%) missing values Missing
total_recomendacoes has 5422 (44.6%) zeros Zeros
aeronave_pmd has 292 (2.4%) zeros Zeros
aeronave_pmd_categoria has 292 (2.4%) zeros Zeros
aeronave_assentos has 557 (4.6%) zeros Zeros
aeronave_ano_fabricacao has 597 (4.9%) zeros Zeros
aeronave_fatalidades_total has 9663 (79.4%) zeros Zeros

Reproduction

Analysis started2022-05-28 14:31:52.033413
Analysis finished2022-05-28 14:32:04.010198
Duration11.98 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

ocorrencia_classificacao
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size190.1 KiB
ACIDENTE
6857 
INCIDENTE
3101 
INCIDENTE GRAVE
2209 

Length

Max length15
Median length8
Mean length9.525766417
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINCIDENTE
2nd rowACIDENTE
3rd rowACIDENTE
4th rowACIDENTE
5th rowACIDENTE

Common Values

ValueCountFrequency (%)
ACIDENTE6857
56.4%
INCIDENTE3101
25.5%
INCIDENTE GRAVE2209
 
18.2%

Length

2022-05-28T11:32:04.075600image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-28T11:32:04.141539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
acidente6857
47.7%
incidente5310
36.9%
grave2209
 
15.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ocorrencia_cidade
Categorical

HIGH CARDINALITY

Distinct1068
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Memory size190.1 KiB
BRASÍLIA - DF
 
581
RIO DE JANEIRO - RJ
 
519
SÃO PAULO - SP
 
343
MANAUS - AM
 
311
BELO HORIZONTE - MG
 
231
Other values (1063)
10182 

Length

Max length37
Median length13
Mean length14.86364757
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique409 ?
Unique (%)3.4%

Sample

1st rowPORTO ALEGRE - RS
2nd rowGUARULHOS - SP
3rd rowGUARULHOS - SP
4th rowGUARULHOS - SP
5th rowGUARULHOS - SP

Common Values

ValueCountFrequency (%)
BRASÍLIA - DF581
 
4.8%
RIO DE JANEIRO - RJ519
 
4.3%
SÃO PAULO - SP343
 
2.8%
MANAUS - AM311
 
2.6%
BELO HORIZONTE - MG231
 
1.9%
GOIÂNIA - GO227
 
1.9%
SANTOS - SP212
 
1.7%
GUARULHOS - SP200
 
1.6%
RECIFE - PE193
 
1.6%
CAMPINAS - SP180
 
1.5%
Other values (1058)9170
75.4%

Length

2022-05-28T11:32:04.228837image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12171
28.2%
sp2514
 
5.8%
pr894
 
2.1%
rs858
 
2.0%
rj789
 
1.8%
pa774
 
1.8%
mg754
 
1.7%
são739
 
1.7%
go725
 
1.7%
rio718
 
1.7%
Other values (1090)22249
51.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ocorrencia_uf
Categorical

HIGH CORRELATION

Distinct28
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size190.1 KiB
SP
2514 
PR
894 
RS
858 
RJ
789 
PA
774 
Other values (23)
6338 

Length

Max length3
Median length2
Mean length2.000328758
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRS
2nd rowSP
3rd rowSP
4th rowSP
5th rowSP

Common Values

ValueCountFrequency (%)
SP2514
20.7%
PR894
 
7.3%
RS858
 
7.1%
RJ789
 
6.5%
PA774
 
6.4%
MG754
 
6.2%
GO725
 
6.0%
AM711
 
5.8%
MT660
 
5.4%
DF581
 
4.8%
Other values (18)2907
23.9%

Length

2022-05-28T11:32:04.323604image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp2514
20.7%
pr894
 
7.3%
rs858
 
7.1%
rj789
 
6.5%
pa774
 
6.4%
mg754
 
6.2%
go725
 
6.0%
am711
 
5.8%
mt660
 
5.4%
df581
 
4.8%
Other values (18)2907
23.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ocorrencia_aerodromo
Categorical

HIGH CARDINALITY

Distinct516
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size190.1 KiB
****
5151 
SBBR
 
536
SBGL
 
203
*****
 
197
SBGR
 
193
Other values (511)
5887 

Length

Max length5
Median length4
Mean length4.016191337
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique190 ?
Unique (%)1.6%

Sample

1st rowSBPA
2nd rowSBGR
3rd rowSBGR
4th rowSBGR
5th rowSBGR

Common Values

ValueCountFrequency (%)
****5151
42.3%
SBBR536
 
4.4%
SBGL203
 
1.7%
*****197
 
1.6%
SBGR193
 
1.6%
SBEG168
 
1.4%
SBGO151
 
1.2%
SBMT145
 
1.2%
SBRF139
 
1.1%
SBBH120
 
1.0%
Other values (506)5164
42.4%

Length

2022-05-28T11:32:04.413348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5376
44.2%
sbbr536
 
4.4%
sbgl203
 
1.7%
sbgr193
 
1.6%
sbeg168
 
1.4%
sbgo151
 
1.2%
sbmt145
 
1.2%
sbrf139
 
1.1%
sbbh120
 
1.0%
sbjd108
 
0.9%
Other values (503)5028
41.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ocorrencia_dia
Categorical

HIGH CARDINALITY

Distinct2684
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Memory size190.1 KiB
09/04/2018
 
234
13/08/2014
 
209
26/06/2015
 
137
06/11/2012
 
117
12/07/2018
 
96
Other values (2679)
11374 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique926 ?
Unique (%)7.6%

Sample

1st row05/01/2012
2nd row06/01/2012
3rd row06/01/2012
4th row06/01/2012
5th row06/01/2012

Common Values

ValueCountFrequency (%)
09/04/2018234
 
1.9%
13/08/2014209
 
1.7%
26/06/2015137
 
1.1%
06/11/2012117
 
1.0%
12/07/201896
 
0.8%
23/09/201592
 
0.8%
28/11/201891
 
0.7%
13/04/201388
 
0.7%
30/11/201785
 
0.7%
16/12/201382
 
0.7%
Other values (2674)10936
89.9%

Length

2022-05-28T11:32:04.502629image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
09/04/2018234
 
1.9%
13/08/2014209
 
1.7%
26/06/2015137
 
1.1%
06/11/2012117
 
1.0%
12/07/201896
 
0.8%
23/09/201592
 
0.8%
28/11/201891
 
0.7%
13/04/201388
 
0.7%
30/11/201785
 
0.7%
16/12/201382
 
0.7%
Other values (2674)10936
89.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ocorrencia_hora
Categorical

HIGH CARDINALITY

Distinct923
Distinct (%)7.6%
Missing1
Missing (%)< 0.1%
Memory size190.1 KiB
20:00:00
 
280
19:00:00
 
252
12:30:00
 
251
00:32:00
 
234
12:00:00
 
220
Other values (918)
10929 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique301 ?
Unique (%)2.5%

Sample

1st row20:27:00
2nd row13:44:00
3rd row13:44:00
4th row13:44:00
5th row13:44:00

Common Values

ValueCountFrequency (%)
20:00:00280
 
2.3%
19:00:00252
 
2.1%
12:30:00251
 
2.1%
00:32:00234
 
1.9%
12:00:00220
 
1.8%
18:00:00217
 
1.8%
14:00:00217
 
1.8%
13:03:00210
 
1.7%
15:30:00188
 
1.5%
15:00:00184
 
1.5%
Other values (913)9913
81.5%

Length

2022-05-28T11:32:04.588438image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20:00:00280
 
2.3%
19:00:00252
 
2.1%
12:30:00251
 
2.1%
00:32:00234
 
1.9%
12:00:00220
 
1.8%
18:00:00217
 
1.8%
14:00:00217
 
1.8%
13:03:00210
 
1.7%
15:30:00188
 
1.5%
15:00:00184
 
1.5%
Other values (913)9913
81.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

divulgacao_relatorio_publicado
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size190.1 KiB
SIM
8172 
NÃO
3995 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNÃO
2nd rowSIM
3rd rowSIM
4th rowSIM
5th rowSIM

Common Values

ValueCountFrequency (%)
SIM8172
67.2%
NÃO3995
32.8%

Length

2022-05-28T11:32:04.672261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-28T11:32:04.731781image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
sim8172
67.2%
não3995
32.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

total_recomendacoes
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.275581491
Minimum0
Maximum13
Zeros5422
Zeros (%)44.6%
Negative0
Negative (%)0.0%
Memory size190.1 KiB
2022-05-28T11:32:04.788325image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile9
Maximum13
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.018723086
Coefficient of variation (CV)1.326572174
Kurtosis2.054938343
Mean2.275581491
Median Absolute Deviation (MAD)1
Skewness1.578543485
Sum27687
Variance9.112689073
MonotonicityNot monotonic
2022-05-28T11:32:04.874540image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
05422
44.6%
21582
 
13.0%
31218
 
10.0%
11027
 
8.4%
4852
 
7.0%
9549
 
4.5%
6486
 
4.0%
7273
 
2.2%
5250
 
2.1%
13208
 
1.7%
Other values (3)300
 
2.5%
ValueCountFrequency (%)
05422
44.6%
11027
 
8.4%
21582
 
13.0%
31218
 
10.0%
4852
 
7.0%
5250
 
2.1%
6486
 
4.0%
7273
 
2.2%
8200
 
1.6%
9549
 
4.5%
ValueCountFrequency (%)
13208
 
1.7%
1212
 
0.1%
1188
 
0.7%
9549
4.5%
8200
 
1.6%
7273
 
2.2%
6486
 
4.0%
5250
 
2.1%
4852
7.0%
31218
10.0%

total_aeronaves_envolvidas
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size190.1 KiB
1
11512 
2
 
646
3
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
111512
94.6%
2646
 
5.3%
39
 
0.1%

Length

2022-05-28T11:32:04.963820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-28T11:32:05.021384image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
111512
94.6%
2646
 
5.3%
39
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ocorrencia_saida_pista
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size190.1 KiB
NÃO
10399 
SIM
1768 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNÃO
2nd rowNÃO
3rd rowNÃO
4th rowNÃO
5th rowNÃO

Common Values

ValueCountFrequency (%)
NÃO10399
85.5%
SIM1768
 
14.5%

Length

2022-05-28T11:32:05.091292image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-28T11:32:05.149325image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
não10399
85.5%
sim1768
 
14.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ocorrencia_localizacao
Categorical

HIGH CARDINALITY
MISSING

Distinct2702
Distinct (%)24.2%
Missing983
Missing (%)8.1%
Memory size190.1 KiB
*** / ***
 
699
-158.711.111.11 / -479.186.111.11
 
236
-23.9597222222 / -46.3269444444
 
208
-3.7325 / -38.7119444444
 
136
\t-25.25833333\t / \t-49.32805556\t
 
117
Other values (2697)
9788 

Length

Max length35
Median length30
Mean length27.78710658
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1692 ?
Unique (%)15.1%

Sample

1st row-23.4355555556 / -46.4730555556
2nd row-23.4355555556 / -46.4730555556
3rd row-23.4355555556 / -46.4730555556
4th row-23.4355555556 / -46.4730555556
5th row-23.4355555556 / -46.4730555556

Common Values

ValueCountFrequency (%)
*** / ***699
 
5.7%
-158.711.111.11 / -479.186.111.11236
 
1.9%
-23.9597222222 / -46.3269444444208
 
1.7%
-3.7325 / -38.7119444444136
 
1.1%
\t-25.25833333\t / \t-49.32805556\t117
 
1.0%
-22.81 / -43.2505555556100
 
0.8%
-215.997.222.22 / -488.327.777.7796
 
0.8%
-15.8691666667 / -47.920833333391
 
0.7%
-11.8961111111 / -44.294444444490
 
0.7%
-30.383.333.333 / -600.62590
 
0.7%
Other values (2692)9321
76.6%
(Missing)983
 
8.1%

Length

2022-05-28T11:32:05.227808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12597
37.5%
479.186.111.11236
 
0.7%
158.711.111.11236
 
0.7%
23.9597222222208
 
0.6%
46.3269444444208
 
0.6%
22.81161
 
0.5%
3.7325136
 
0.4%
38.7119444444136
 
0.4%
t-25.25833333\t117
 
0.3%
t-49.32805556\t117
 
0.3%
Other values (5150)19430
57.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ocorrencia_tipo
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct80
Distinct (%)0.7%
Missing1
Missing (%)< 0.1%
Memory size190.1 KiB
PERDA DE CONTROLE EM VOO
1461 
FALHA DO MOTOR EM VOO
1254 
PERDA DE CONTROLE NO SOLO
1047 
FALHA OU MAU FUNCIONAMENTO DE SISTEMA / COMPONENTE
832 
COLISÃO COM OBSTÁCULO DURANTE A DECOLAGEM E POUSO
710 
Other values (75)
6862 

Length

Max length92
Median length21
Mean length23.7123952
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowESTOURO DE PNEU
2nd rowCOM PESSOAL EM VOO
3rd rowCOM PESSOAL EM VOO
4th rowCOM PESSOAL EM VOO
5th rowCOM PESSOAL EM VOO

Common Values

ValueCountFrequency (%)
PERDA DE CONTROLE EM VOO1461
 
12.0%
FALHA DO MOTOR EM VOO1254
 
10.3%
PERDA DE CONTROLE NO SOLO1047
 
8.6%
FALHA OU MAU FUNCIONAMENTO DE SISTEMA / COMPONENTE832
 
6.8%
COLISÃO COM OBSTÁCULO DURANTE A DECOLAGEM E POUSO710
 
5.8%
EXCURSÃO DE PISTA624
 
5.1%
ESTOURO DE PNEU591
 
4.9%
OUTROS539
 
4.4%
PANE SECA427
 
3.5%
COM TREM DE POUSO393
 
3.2%
Other values (70)4288
35.2%

Length

2022-05-28T11:32:05.348337image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de5501
 
10.4%
em3476
 
6.6%
voo3306
 
6.2%
perda2620
 
5.0%
controle2508
 
4.7%
com2228
 
4.2%
falha2200
 
4.2%
pouso1968
 
3.7%
no1605
 
3.0%
solo1605
 
3.0%
Other values (125)25906
49.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ocorrencia_tipo_categoria
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct80
Distinct (%)0.7%
Missing1
Missing (%)< 0.1%
Memory size190.1 KiB
PERDA DE CONTROLE EM VOO
1461 
FALHA OU MAU FUNCIONAMENTO DO MOTOR | FALHA DO MOTOR EM VOO
1254 
PERDA DE CONTROLE NO SOLO
1047 
FALHA OU MAU FUNCIONAMENTO DE SISTEMA / COMPONENTE
832 
COLISÃO COM OBSTÁCULO DURANTE A DECOLAGEM E POUSO
710 
Other values (75)
6862 

Length

Max length96
Median length35
Mean length38.44065428
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowFALHA OU MAU FUNCIONAMENTO DE SISTEMA / COMPONENTE | ESTOURO DE PNEU
2nd rowOUTROS | COM PESSOAL EM VOO
3rd rowOUTROS | COM PESSOAL EM VOO
4th rowOUTROS | COM PESSOAL EM VOO
5th rowOUTROS | COM PESSOAL EM VOO

Common Values

ValueCountFrequency (%)
PERDA DE CONTROLE EM VOO1461
 
12.0%
FALHA OU MAU FUNCIONAMENTO DO MOTOR | FALHA DO MOTOR EM VOO1254
 
10.3%
PERDA DE CONTROLE NO SOLO1047
 
8.6%
FALHA OU MAU FUNCIONAMENTO DE SISTEMA / COMPONENTE832
 
6.8%
COLISÃO COM OBSTÁCULO DURANTE A DECOLAGEM E POUSO710
 
5.8%
EXCURSÃO DE PISTA624
 
5.1%
FALHA OU MAU FUNCIONAMENTO DE SISTEMA / COMPONENTE | ESTOURO DE PNEU591
 
4.9%
OUTROS539
 
4.4%
COMBUSTÍVEL | PANE SECA427
 
3.5%
FALHA OU MAU FUNCIONAMENTO DE SISTEMA / COMPONENTE | COM TREM DE POUSO393
 
3.2%
Other values (70)4288
35.2%

Length

2022-05-28T11:32:05.465889image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8243
 
9.6%
de7448
 
8.7%
falha4972
 
5.8%
em3812
 
4.5%
ou3655
 
4.3%
voo3642
 
4.3%
mau3633
 
4.2%
funcionamento3633
 
4.2%
perda2956
 
3.5%
do2699
 
3.2%
Other values (125)40907
47.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

taxonomia_tipo_icao
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct32
Distinct (%)0.3%
Missing1
Missing (%)< 0.1%
Memory size190.1 KiB
SCF-NP
2229 
LOC-I
1461 
SCF-PP
1404 
OTHR
1287 
LOC-G
1047 
Other values (27)
4738 

Length

Max length7
Median length5
Mean length4.558112773
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSCF-NP
2nd rowOTHR
3rd rowOTHR
4th rowOTHR
5th rowOTHR

Common Values

ValueCountFrequency (%)
SCF-NP2229
18.3%
LOC-I1461
12.0%
SCF-PP1404
11.5%
OTHR1287
10.6%
LOC-G1047
8.6%
RE838
 
6.9%
CTOL710
 
5.8%
FUEL455
 
3.7%
GCOL388
 
3.2%
ARC352
 
2.9%
Other values (22)1995
16.4%

Length

2022-05-28T11:32:05.577488image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
scf-np2229
18.3%
loc-i1461
12.0%
scf-pp1404
11.5%
othr1287
10.6%
loc-g1047
8.6%
re838
 
6.9%
ctol710
 
5.8%
fuel455
 
3.7%
gcol388
 
3.2%
arc352
 
2.9%
Other values (22)1995
16.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aeronave_matricula
Categorical

HIGH CARDINALITY

Distinct3902
Distinct (%)32.1%
Missing0
Missing (%)0.0%
Memory size190.1 KiB
PRAFA
 
209
PREES
 
136
PRGTN
 
117
PTMFW
 
117
FAB2345
 
117
Other values (3897)
11471 

Length

Max length8
Median length5
Mean length5.036081203
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2427 ?
Unique (%)19.9%

Sample

1st rowPRCDL
2nd rowPRTKB
3rd rowPRTKB
4th rowPRTKB
5th rowPRTKB

Common Values

ValueCountFrequency (%)
PRAFA209
 
1.7%
PREES136
 
1.1%
PRGTN117
 
1.0%
PTMFW117
 
1.0%
FAB2345117
 
1.0%
YV293790
 
0.7%
PPELA90
 
0.7%
CSTOF88
 
0.7%
PRMBG81
 
0.7%
PTCNL78
 
0.6%
Other values (3892)11044
90.8%

Length

2022-05-28T11:32:05.673712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
prafa209
 
1.7%
prees136
 
1.1%
prgtn117
 
1.0%
fab2345117
 
1.0%
ptmfw117
 
1.0%
yv293790
 
0.7%
ppela90
 
0.7%
cstof88
 
0.7%
prmbg81
 
0.7%
ptcnl78
 
0.6%
Other values (3892)11044
90.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aeronave_operador_categoria
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size190.1 KiB
***
9863 
REGULAR
 
639
PARTICULAR
 
594
TÁXI AÉREO
 
325
INSTRUÇÃO
 
307
Other values (6)
 
439

Length

Max length20
Median length3
Mean length4.26349963
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPARTICULAR
2nd row***
3rd row***
4th row***
5th row***

Common Values

ValueCountFrequency (%)
***9863
81.1%
REGULAR639
 
5.3%
PARTICULAR594
 
4.9%
TÁXI AÉREO325
 
2.7%
INSTRUÇÃO307
 
2.5%
EXPERIMENTAL283
 
2.3%
ADMINISTRAÇÃO DIRETA89
 
0.7%
AGRÍCOLA24
 
0.2%
ESPECIALIZADA21
 
0.2%
NÃO REGULAR14
 
0.1%

Length

2022-05-28T11:32:05.773904image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9863
78.3%
regular653
 
5.2%
particular594
 
4.7%
táxi325
 
2.6%
aéreo325
 
2.6%
instrução307
 
2.4%
experimental283
 
2.2%
administração89
 
0.7%
direta89
 
0.7%
agrícola24
 
0.2%
Other values (3)43
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aeronave_tipo_veiculo
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size190.1 KiB
AVIÃO
9424 
HELICÓPTERO
1995 
ULTRALEVE
 
425
***
 
196
PLANADOR
 
87
Other values (5)
 
40

Length

Max length11
Median length5
Mean length6.118928249
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowAVIÃO
2nd rowAVIÃO
3rd rowAVIÃO
4th rowAVIÃO
5th rowAVIÃO

Common Values

ValueCountFrequency (%)
AVIÃO9424
77.5%
HELICÓPTERO1995
 
16.4%
ULTRALEVE425
 
3.5%
***196
 
1.6%
PLANADOR87
 
0.7%
ANFÍBIO31
 
0.3%
TRIKE5
 
< 0.1%
DIRIGÍVEL2
 
< 0.1%
HIDROAVIÃO1
 
< 0.1%
BALÃO1
 
< 0.1%

Length

2022-05-28T11:32:05.872112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-28T11:32:05.942047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
avião9424
77.5%
helicóptero1995
 
16.4%
ultraleve425
 
3.5%
196
 
1.6%
planador87
 
0.7%
anfíbio31
 
0.3%
trike5
 
< 0.1%
dirigível2
 
< 0.1%
hidroavião1
 
< 0.1%
balão1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aeronave_fabricante
Categorical

HIGH CARDINALITY

Distinct234
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size190.1 KiB
CESSNA AIRCRAFT
2339 
NEIVA INDUSTRIA AERONAUTICA
1452 
EMBRAER
1099 
PIPER AIRCRAFT
890 
BOEING COMPANY
626 
Other values (229)
5761 

Length

Max length47
Median length15
Mean length15.48212378
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique125 ?
Unique (%)1.0%

Sample

1st rowRAYTHEON AIRCRAFT
2nd rowAEROSPATIALE AND ALENIA
3rd rowAEROSPATIALE AND ALENIA
4th rowAEROSPATIALE AND ALENIA
5th rowAEROSPATIALE AND ALENIA

Common Values

ValueCountFrequency (%)
CESSNA AIRCRAFT2339
19.2%
NEIVA INDUSTRIA AERONAUTICA1452
11.9%
EMBRAER1099
 
9.0%
PIPER AIRCRAFT890
 
7.3%
BOEING COMPANY626
 
5.1%
AIRBUS INDUSTRIE552
 
4.5%
BEECH AIRCRAFT544
 
4.5%
BELL HELICOPTER423
 
3.5%
***421
 
3.5%
AERO BOERO407
 
3.3%
Other values (224)3414
28.1%

Length

2022-05-28T11:32:06.085021image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aircraft4119
16.7%
cessna2339
 
9.5%
aeronautica1554
 
6.3%
industria1542
 
6.3%
neiva1452
 
5.9%
embraer1099
 
4.5%
piper890
 
3.6%
helicopter878
 
3.6%
company635
 
2.6%
boeing627
 
2.5%
Other values (425)9471
38.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aeronave_modelo
Categorical

HIGH CARDINALITY

Distinct738
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size190.1 KiB
AB-115
 
355
737-8EH
 
309
EMB-201A
 
303
EMB-202
 
286
ATR-72-212A
 
278
Other values (733)
10636 

Length

Max length29
Median length7
Mean length6.881564889
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique309 ?
Unique (%)2.5%

Sample

1st row58
2nd rowATR-42-500
3rd rowATR-42-500
4th rowATR-42-500
5th rowATR-42-500

Common Values

ValueCountFrequency (%)
AB-115355
 
2.9%
737-8EH309
 
2.5%
EMB-201A303
 
2.5%
EMB-202286
 
2.4%
ATR-72-212A278
 
2.3%
AS 350 B2273
 
2.2%
152238
 
2.0%
EMB-202A217
 
1.8%
560XLS+209
 
1.7%
***209
 
1.7%
Other values (728)9490
78.0%

Length

2022-05-28T11:32:06.188629image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
as514
 
3.4%
350459
 
3.0%
ab-115355
 
2.3%
737-8eh309
 
2.0%
emb-201a303
 
2.0%
emb-202287
 
1.9%
atr-72-212a278
 
1.8%
b2273
 
1.8%
erj270
 
1.8%
r44248
 
1.6%
Other values (815)11839
78.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aeronave_tipo_icao
Categorical

HIGH CARDINALITY

Distinct232
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size190.1 KiB
IPAN
 
870
PA34
 
552
AS50
 
490
ULAC
 
364
AB11
 
355
Other values (227)
9536 

Length

Max length5
Median length4
Mean length3.89348237
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)0.4%

Sample

1st rowBE58
2nd rowAT45
3rd rowAT45
4th rowAT45
5th rowAT45

Common Values

ValueCountFrequency (%)
IPAN870
 
7.2%
PA34552
 
4.5%
AS50490
 
4.0%
ULAC364
 
3.0%
AB11355
 
2.9%
***329
 
2.7%
B738319
 
2.6%
A320317
 
2.6%
B06302
 
2.5%
C172275
 
2.3%
Other values (222)7994
65.7%

Length

2022-05-28T11:32:06.297750image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ipan870
 
7.1%
pa34552
 
4.5%
as50490
 
4.0%
ulac364
 
3.0%
ab11355
 
2.9%
331
 
2.7%
b738319
 
2.6%
a320317
 
2.6%
b06302
 
2.5%
c172275
 
2.3%
Other values (221)7993
65.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aeronave_motor_tipo
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing5
Missing (%)< 0.1%
Memory size190.1 KiB
PISTÃO
6239 
JATO
2182 
TURBOEIXO
1703 
TURBOÉLICE
1670 
***
 
281

Length

Max length10
Median length6
Mean length6.569807597
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPISTÃO
2nd rowTURBOÉLICE
3rd rowTURBOÉLICE
4th rowTURBOÉLICE
5th rowTURBOÉLICE

Common Values

ValueCountFrequency (%)
PISTÃO6239
51.3%
JATO2182
 
17.9%
TURBOEIXO1703
 
14.0%
TURBOÉLICE1670
 
13.7%
***281
 
2.3%
SEM TRAÇÃO87
 
0.7%
(Missing)5
 
< 0.1%

Length

2022-05-28T11:32:06.403893image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-28T11:32:06.471349image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
pistão6239
50.9%
jato2182
 
17.8%
turboeixo1703
 
13.9%
turboélice1670
 
13.6%
281
 
2.3%
sem87
 
0.7%
tração87
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aeronave_motor_quantidade
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size190.1 KiB
MONOMOTOR
6786 
BIMOTOR
4919 
SEM TRAÇÃO
 
276
***
 
113
TRIMOTOR
 
68

Length

Max length11
Median length9
Mean length8.15361223
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBIMOTOR
2nd rowBIMOTOR
3rd rowBIMOTOR
4th rowBIMOTOR
5th rowBIMOTOR

Common Values

ValueCountFrequency (%)
MONOMOTOR6786
55.8%
BIMOTOR4919
40.4%
SEM TRAÇÃO276
 
2.3%
***113
 
0.9%
TRIMOTOR68
 
0.6%
QUADRIMOTOR5
 
< 0.1%

Length

2022-05-28T11:32:06.578592image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-28T11:32:06.651007image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
monomotor6786
54.5%
bimotor4919
39.5%
sem276
 
2.2%
tração276
 
2.2%
113
 
0.9%
trimotor68
 
0.5%
quadrimotor5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aeronave_pmd
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct459
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14352.33821
Minimum0
Maximum396895
Zeros292
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size190.1 KiB
2022-05-28T11:32:06.760643image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile587
Q11224
median2018
Q34920
95-th percentile75500
Maximum396895
Range396895
Interquartile range (IQR)3696

Descriptive statistics

Standard deviation38808.99191
Coefficient of variation (CV)2.70401877
Kurtosis32.77249296
Mean14352.33821
Median Absolute Deviation (MAD)1248
Skewness5.160210489
Sum174624899
Variance1506137853
MonotonicityNot monotonic
2022-05-28T11:32:06.871231image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800855
 
7.0%
1633388
 
3.2%
3629384
 
3.2%
770355
 
2.9%
2155299
 
2.5%
2250296
 
2.4%
70533295
 
2.4%
0292
 
2.4%
1724241
 
2.0%
1315234
 
1.9%
Other values (449)8528
70.1%
ValueCountFrequency (%)
0292
2.4%
351
 
< 0.1%
2081
 
< 0.1%
2503
 
< 0.1%
2805
 
< 0.1%
3001
 
< 0.1%
3081
 
< 0.1%
3401
 
< 0.1%
3421
 
< 0.1%
3521
 
< 0.1%
ValueCountFrequency (%)
3968951
 
< 0.1%
3855531
 
< 0.1%
3807901
 
< 0.1%
3515341
 
< 0.1%
34654446
0.4%
2859904
 
< 0.1%
2630801
 
< 0.1%
2562801
 
< 0.1%
2530001
 
< 0.1%
2472001
 
< 0.1%

aeronave_pmd_categoria
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct459
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14352.33821
Minimum0
Maximum396895
Zeros292
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size190.1 KiB
2022-05-28T11:32:06.991760image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile587
Q11224
median2018
Q34920
95-th percentile75500
Maximum396895
Range396895
Interquartile range (IQR)3696

Descriptive statistics

Standard deviation38808.99191
Coefficient of variation (CV)2.70401877
Kurtosis32.77249296
Mean14352.33821
Median Absolute Deviation (MAD)1248
Skewness5.160210489
Sum174624899
Variance1506137853
MonotonicityNot monotonic
2022-05-28T11:32:07.278943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800855
 
7.0%
1633388
 
3.2%
3629384
 
3.2%
770355
 
2.9%
2155299
 
2.5%
2250296
 
2.4%
70533295
 
2.4%
0292
 
2.4%
1724241
 
2.0%
1315234
 
1.9%
Other values (449)8528
70.1%
ValueCountFrequency (%)
0292
2.4%
351
 
< 0.1%
2081
 
< 0.1%
2503
 
< 0.1%
2805
 
< 0.1%
3001
 
< 0.1%
3081
 
< 0.1%
3401
 
< 0.1%
3421
 
< 0.1%
3521
 
< 0.1%
ValueCountFrequency (%)
3968951
 
< 0.1%
3855531
 
< 0.1%
3807901
 
< 0.1%
3515341
 
< 0.1%
34654446
0.4%
2859904
 
< 0.1%
2630801
 
< 0.1%
2562801
 
< 0.1%
2530001
 
< 0.1%
2472001
 
< 0.1%

aeronave_assentos
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct86
Distinct (%)0.7%
Missing241
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean23.95614624
Minimum0
Maximum384
Zeros557
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size190.1 KiB
2022-05-28T11:32:07.395999image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median6
Q39
95-th percentile182
Maximum384
Range384
Interquartile range (IQR)7

Descriptive statistics

Standard deviation54.11197805
Coefficient of variation (CV)2.25879311
Kurtosis10.96164395
Mean23.95614624
Median Absolute Deviation (MAD)4
Skewness3.169524776
Sum285701
Variance2928.106169
MonotonicityNot monotonic
2022-05-28T11:32:07.507103image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61960
16.1%
11601
13.2%
21575
12.9%
41334
11.0%
7901
 
7.4%
0557
 
4.6%
10479
 
3.9%
8392
 
3.2%
14322
 
2.6%
5319
 
2.6%
Other values (76)2486
20.4%
ValueCountFrequency (%)
0557
 
4.6%
11601
13.2%
21575
12.9%
3180
 
1.5%
41334
11.0%
5319
 
2.6%
61960
16.1%
7901
7.4%
8392
 
3.2%
9204
 
1.7%
ValueCountFrequency (%)
38444
0.4%
3822
 
< 0.1%
3121
 
< 0.1%
2882
 
< 0.1%
2842
 
< 0.1%
2782
 
< 0.1%
2581
 
< 0.1%
2431
 
< 0.1%
2423
 
< 0.1%
24023
0.2%

aeronave_ano_fabricacao
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct80
Distinct (%)0.7%
Missing246
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean1893.589883
Minimum0
Maximum9999
Zeros597
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size190.1 KiB
2022-05-28T11:32:07.632591image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11978
median1992
Q32007
95-th percentile2013
Maximum9999
Range9999
Interquartile range (IQR)29

Descriptive statistics

Standard deviation441.2069936
Coefficient of variation (CV)0.2330002909
Kurtosis23.5639855
Mean1893.589883
Median Absolute Deviation (MAD)15
Skewness-3.428263932
Sum22573485
Variance194663.6112
MonotonicityNot monotonic
2022-05-28T11:32:07.744603image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2010686
 
5.6%
0597
 
4.9%
2012469
 
3.9%
2007399
 
3.3%
2013393
 
3.2%
1979382
 
3.1%
2011372
 
3.1%
2001330
 
2.7%
2008323
 
2.7%
1992315
 
2.6%
Other values (70)7655
62.9%
ValueCountFrequency (%)
0597
4.9%
19361
 
< 0.1%
19401
 
< 0.1%
19454
 
< 0.1%
194635
 
0.3%
19477
 
0.1%
19487
 
0.1%
19491
 
< 0.1%
195026
 
0.2%
195152
 
0.4%
ValueCountFrequency (%)
99991
 
< 0.1%
20203
 
< 0.1%
201912
 
0.1%
201831
 
0.3%
201733
 
0.3%
201669
 
0.6%
201580
 
0.7%
201466
 
0.5%
2013393
3.2%
2012469
3.9%

aeronave_registro_categoria
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size190.1 KiB
AVIÃO
9424 
HELICÓPTERO
1995 
ULTRALEVE
 
425
***
 
196
PLANADOR
 
87
Other values (5)
 
40

Length

Max length11
Median length5
Mean length6.118928249
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowAVIÃO
2nd rowAVIÃO
3rd rowAVIÃO
4th rowAVIÃO
5th rowAVIÃO

Common Values

ValueCountFrequency (%)
AVIÃO9424
77.5%
HELICÓPTERO1995
 
16.4%
ULTRALEVE425
 
3.5%
***196
 
1.6%
PLANADOR87
 
0.7%
ANFÍBIO31
 
0.3%
TRIKE5
 
< 0.1%
DIRIGÍVEL2
 
< 0.1%
HIDROAVIÃO1
 
< 0.1%
BALÃO1
 
< 0.1%

Length

2022-05-28T11:32:07.858180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-28T11:32:07.930101image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
avião9424
77.5%
helicóptero1995
 
16.4%
ultraleve425
 
3.5%
196
 
1.6%
planador87
 
0.7%
anfíbio31
 
0.3%
trike5
 
< 0.1%
dirigível2
 
< 0.1%
hidroavião1
 
< 0.1%
balão1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aeronave_registro_segmento
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size190.1 KiB
PARTICULAR
3785 
REGULAR
1873 
INSTRUÇÃO
1757 
TÁXI AÉREO
1483 
AGRÍCOLA
1033 
Other values (8)
2236 

Length

Max length22
Median length10
Mean length10.03747843
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPARTICULAR
2nd rowREGULAR
3rd rowREGULAR
4th rowREGULAR
5th rowREGULAR

Common Values

ValueCountFrequency (%)
PARTICULAR3785
31.1%
REGULAR1873
15.4%
INSTRUÇÃO1757
14.4%
TÁXI AÉREO1483
 
12.2%
AGRÍCOLA1033
 
8.5%
ADMINISTRAÇÃO DIRETA835
 
6.9%
EXPERIMENTAL579
 
4.8%
ESPECIALIZADA322
 
2.6%
***224
 
1.8%
MÚLTIPLA131
 
1.1%
Other values (3)145
 
1.2%

Length

2022-05-28T11:32:08.075925image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
particular3785
25.9%
regular1916
13.1%
instrução1757
12.0%
táxi1483
 
10.1%
aéreo1483
 
10.1%
agrícola1033
 
7.1%
administração936
 
6.4%
direta835
 
5.7%
experimental579
 
4.0%
especializada322
 
2.2%
Other values (5)500
 
3.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aeronave_voo_origem
Categorical

HIGH CARDINALITY

Distinct678
Distinct (%)5.6%
Missing1
Missing (%)< 0.1%
Memory size190.1 KiB
FORA DE AERODROMO
3615 
PRESIDENTE JUSCELINO KUBITSCHEK
 
406
SANTOS DUMONT
 
311
***
 
263
PINTO MARTINS
 
179
Other values (673)
7392 

Length

Max length63
Median length17
Mean length17.52548085
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique255 ?
Unique (%)2.1%

Sample

1st rowFORA DE AERODROMO
2nd rowFORA DE AERODROMO
3rd rowFORA DE AERODROMO
4th rowFORA DE AERODROMO
5th rowFORA DE AERODROMO

Common Values

ValueCountFrequency (%)
FORA DE AERODROMO3615
29.7%
PRESIDENTE JUSCELINO KUBITSCHEK406
 
3.3%
SANTOS DUMONT311
 
2.6%
***263
 
2.2%
PINTO MARTINS179
 
1.5%
VAL DE CANS / JÚLIO CEZAR RIBEIRO151
 
1.2%
AEROPORTO ESTADUAL DE JUNDIAÍ148
 
1.2%
CAMPO DE MARTE146
 
1.2%
GUARARAPES - GILBERTO FREYRE142
 
1.2%
DEPUTADO LUÍS EDUARDO MAGALHÃES142
 
1.2%
Other values (668)6663
54.8%

Length

2022-05-28T11:32:08.200510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de5075
 
15.3%
aerodromo3617
 
10.9%
fora3615
 
10.9%
803
 
2.4%
presidente448
 
1.4%
juscelino412
 
1.2%
kubitschek406
 
1.2%
santos313
 
0.9%
dumont311
 
0.9%
santa295
 
0.9%
Other values (959)17844
53.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aeronave_voo_destino
Categorical

HIGH CARDINALITY

Distinct675
Distinct (%)5.5%
Missing1
Missing (%)< 0.1%
Memory size190.1 KiB
FORA DE AERODROMO
4021 
PRESIDENTE JUSCELINO KUBITSCHEK
 
256
***
 
247
BASE AÉREA DE SANTOS
 
210
BACACHERI
 
200
Other values (670)
7232 

Length

Max length50
Median length17
Mean length17.07767549
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique256 ?
Unique (%)2.1%

Sample

1st rowFORA DE AERODROMO
2nd rowFORA DE AERODROMO
3rd rowFORA DE AERODROMO
4th rowFORA DE AERODROMO
5th rowFORA DE AERODROMO

Common Values

ValueCountFrequency (%)
FORA DE AERODROMO4021
33.0%
PRESIDENTE JUSCELINO KUBITSCHEK256
 
2.1%
***247
 
2.0%
BASE AÉREA DE SANTOS210
 
1.7%
BACACHERI200
 
1.6%
EDUARDO GOMES177
 
1.5%
CAMPO DE MARTE145
 
1.2%
GUARARAPES - GILBERTO FREYRE142
 
1.2%
MARECHAL CUNHA MACHADO130
 
1.1%
AEROCLUBE DE ITÁPOLIS127
 
1.0%
Other values (665)6511
53.5%

Length

2022-05-28T11:32:08.328450image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de5515
 
16.9%
aerodromo4031
 
12.4%
fora4021
 
12.3%
765
 
2.3%
fazenda339
 
1.0%
santos305
 
0.9%
presidente290
 
0.9%
aeroclube266
 
0.8%
campo264
 
0.8%
carlos262
 
0.8%
Other values (925)16557
50.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aeronave_fase_operacao
Categorical

HIGH CORRELATION

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size190.1 KiB
DECOLAGEM
2138 
POUSO
1999 
CRUZEIRO
1660 
CORRIDA APÓS POUSO
1232 
APROXIMAÇÃO FINAL
802 
Other values (27)
4336 

Length

Max length31
Median length9
Mean length10.09895619
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowPOUSO
2nd rowDESCIDA
3rd rowDESCIDA
4th rowDESCIDA
5th rowDESCIDA

Common Values

ValueCountFrequency (%)
DECOLAGEM2138
17.6%
POUSO1999
16.4%
CRUZEIRO1660
13.6%
CORRIDA APÓS POUSO1232
10.1%
APROXIMAÇÃO FINAL802
 
6.6%
MANOBRA617
 
5.1%
SUBIDA596
 
4.9%
TÁXI559
 
4.6%
CIRCUITO DE TRÁFEGO438
 
3.6%
ESPECIALIZADA390
 
3.2%
Other values (22)1736
14.3%

Length

2022-05-28T11:32:08.435336image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pouso3231
18.2%
decolagem2186
12.3%
cruzeiro1660
 
9.4%
corrida1232
 
6.9%
após1232
 
6.9%
final823
 
4.6%
aproximação821
 
4.6%
manobra617
 
3.5%
subida596
 
3.4%
táxi559
 
3.1%
Other values (40)4794
27.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aeronave_tipo_operacao
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size190.1 KiB
PRIVADA
3959 
REGULAR
1891 
INSTRUÇÃO
1698 
AGRÍCOLA
1466 
TÁXI AÉREO
1454 
Other values (5)
1699 

Length

Max length13
Median length8
Mean length8.082353908
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPRIVADA
2nd rowREGULAR
3rd rowREGULAR
4th rowREGULAR
5th rowREGULAR

Common Values

ValueCountFrequency (%)
PRIVADA3959
32.5%
REGULAR1891
15.5%
INSTRUÇÃO1698
14.0%
AGRÍCOLA1466
 
12.0%
TÁXI AÉREO1454
 
12.0%
POLICIAL757
 
6.2%
ESPECIALIZADA330
 
2.7%
EXPERIMENTAL248
 
2.0%
***186
 
1.5%
NÃO REGULAR178
 
1.5%

Length

2022-05-28T11:32:08.527056image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-28T11:32:08.591537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
privada3959
28.7%
regular2069
15.0%
instrução1698
12.3%
agrícola1466
 
10.6%
táxi1454
 
10.5%
aéreo1454
 
10.5%
policial757
 
5.5%
especializada330
 
2.4%
experimental248
 
1.8%
186
 
1.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aeronave_nivel_dano
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size190.1 KiB
SUBSTANCIAL
4936 
NENHUM
2686 
LEVE
2502 
DESTRUÍDA
1976 
***
 
67

Length

Max length11
Median length9
Mean length8.087860607
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLEVE
2nd rowNENHUM
3rd rowNENHUM
4th rowNENHUM
5th rowNENHUM

Common Values

ValueCountFrequency (%)
SUBSTANCIAL4936
40.6%
NENHUM2686
22.1%
LEVE2502
20.6%
DESTRUÍDA1976
16.2%
***67
 
0.6%

Length

2022-05-28T11:32:08.731409image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-28T11:32:08.798865image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
substancial4936
40.6%
nenhum2686
22.1%
leve2502
20.6%
destruída1976
16.2%
67
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aeronave_fatalidades_total
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.629325224
Minimum0
Maximum10
Zeros9663
Zeros (%)79.4%
Negative0
Negative (%)0.0%
Memory size190.1 KiB
2022-05-28T11:32:08.882717image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.578934584
Coefficient of variation (CV)2.508932622
Kurtosis9.186701556
Mean0.629325224
Median Absolute Deviation (MAD)0
Skewness2.992748752
Sum7657
Variance2.493034421
MonotonicityNot monotonic
2022-05-28T11:32:08.959857image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
09663
79.4%
1873
 
7.2%
2445
 
3.7%
4409
 
3.4%
5298
 
2.4%
7210
 
1.7%
3176
 
1.4%
1039
 
0.3%
828
 
0.2%
626
 
0.2%
ValueCountFrequency (%)
09663
79.4%
1873
 
7.2%
2445
 
3.7%
3176
 
1.4%
4409
 
3.4%
5298
 
2.4%
626
 
0.2%
7210
 
1.7%
828
 
0.2%
1039
 
0.3%
ValueCountFrequency (%)
1039
 
0.3%
828
 
0.2%
7210
 
1.7%
626
 
0.2%
5298
 
2.4%
4409
 
3.4%
3176
 
1.4%
2445
 
3.7%
1873
 
7.2%
09663
79.4%

fator_nome
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct74
Distinct (%)1.0%
Missing4432
Missing (%)36.4%
Memory size190.1 KiB
JULGAMENTO DE PILOTAGEM
821 
APLICAÇÃO DE COMANDOS
584 
SUPERVISÃO GERENCIAL
525 
PLANEJAMENTO DE VOO
 
426
MANUTENÇÃO DA AERONAVE
 
400
Other values (69)
4979 

Length

Max length41
Median length21
Mean length19.16354234
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowAPLICAÇÃO DE COMANDOS
2nd rowAPLICAÇÃO DE COMANDOS
3rd rowAPLICAÇÃO DE COMANDOS
4th rowATENÇÃO
5th rowATENÇÃO

Common Values

ValueCountFrequency (%)
JULGAMENTO DE PILOTAGEM821
 
6.7%
APLICAÇÃO DE COMANDOS584
 
4.8%
SUPERVISÃO GERENCIAL525
 
4.3%
PLANEJAMENTO DE VOO426
 
3.5%
MANUTENÇÃO DA AERONAVE400
 
3.3%
PROCESSO DECISÓRIO397
 
3.3%
ATITUDE388
 
3.2%
PERCEPÇÃO323
 
2.7%
POUCA EXPERIÊNCIA DO PILOTO269
 
2.2%
INDISCIPLINA DE VOO223
 
1.8%
Other values (64)3379
27.8%
(Missing)4432
36.4%

Length

2022-05-28T11:32:09.063493image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de2844
 
15.0%
pilotagem821
 
4.3%
julgamento821
 
4.3%
do692
 
3.7%
voo649
 
3.4%
gerencial626
 
3.3%
comandos584
 
3.1%
aplicação584
 
3.1%
supervisão554
 
2.9%
planejamento547
 
2.9%
Other values (110)10216
53.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

fator_aspecto
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct12
Distinct (%)0.2%
Missing4432
Missing (%)36.4%
Memory size190.1 KiB
DESEMPENHO DO SER HUMANO
4062 
ASPECTO PSICOLÓGICO
2830 
ELEMENTOS RELACIONADOS AO AMBIENTE OPERACIONAL
 
191
INFRAESTRUTURA AEROPORTUÁRIA
 
186
ASPECTO MÉDICO
 
172
Other values (7)
 
294

Length

Max length46
Median length24
Mean length22.13587589
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDESEMPENHO DO SER HUMANO
2nd rowDESEMPENHO DO SER HUMANO
3rd rowDESEMPENHO DO SER HUMANO
4th rowASPECTO PSICOLÓGICO
5th rowASPECTO PSICOLÓGICO

Common Values

ValueCountFrequency (%)
DESEMPENHO DO SER HUMANO4062
33.4%
ASPECTO PSICOLÓGICO2830
23.3%
ELEMENTOS RELACIONADOS AO AMBIENTE OPERACIONAL191
 
1.6%
INFRAESTRUTURA AEROPORTUÁRIA186
 
1.5%
ASPECTO MÉDICO172
 
1.4%
OUTRO119
 
1.0%
ERGONOMIA36
 
0.3%
INFRAESTRUTURA DE TRÁFEGO AÉREO35
 
0.3%
***35
 
0.3%
ASPECTO DE FABRICAÇÃO31
 
0.3%
Other values (2)38
 
0.3%
(Missing)4432
36.4%

Length

2022-05-28T11:32:09.170628image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
do4071
16.9%
desempenho4062
16.8%
ser4062
16.8%
humano4062
16.8%
aspecto3071
12.7%
psicológico2830
11.7%
infraestrutura221
 
0.9%
elementos191
 
0.8%
relacionados191
 
0.8%
ao191
 
0.8%
Other values (14)1182
 
4.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

fator_condicionante
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct7
Distinct (%)0.1%
Missing4432
Missing (%)36.4%
Memory size190.1 KiB
OPERAÇÃO DA AERONAVE
3457 
INDIVIDUAL
1563 
ORGANIZACIONAL
910 
***
843 
MANUTENÇÃO DA AERONAVE
400 
Other values (2)
562 

Length

Max length38
Median length20
Mean length15.63193277
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOPERAÇÃO DA AERONAVE
2nd rowOPERAÇÃO DA AERONAVE
3rd rowOPERAÇÃO DA AERONAVE
4th rowINDIVIDUAL
5th rowINDIVIDUAL

Common Values

ValueCountFrequency (%)
OPERAÇÃO DA AERONAVE3457
28.4%
INDIVIDUAL1563
 
12.8%
ORGANIZACIONAL910
 
7.5%
***843
 
6.9%
MANUTENÇÃO DA AERONAVE400
 
3.3%
PSICOSSOCIAL357
 
2.9%
PRESTAÇÃO DE SERVIÇOS DE TRÁFEGO AÉREO205
 
1.7%
(Missing)4432
36.4%

Length

2022-05-28T11:32:09.265364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-28T11:32:09.336292image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
da3857
23.4%
aeronave3857
23.4%
operação3457
21.0%
individual1563
9.5%
organizacional910
 
5.5%
843
 
5.1%
de410
 
2.5%
manutenção400
 
2.4%
psicossocial357
 
2.2%
prestação205
 
1.2%
Other values (3)615
 
3.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

fator_area
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.1%
Missing4432
Missing (%)36.4%
Memory size190.1 KiB
FATOR OPERACIONAL
4474 
FATOR HUMANO
3038 
OUTRO
 
119
FATOR MATERIAL
 
69
***
 
35

Length

Max length17
Median length17
Mean length14.76147382
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFATOR OPERACIONAL
2nd rowFATOR OPERACIONAL
3rd rowFATOR OPERACIONAL
4th rowFATOR HUMANO
5th rowFATOR HUMANO

Common Values

ValueCountFrequency (%)
FATOR OPERACIONAL4474
36.8%
FATOR HUMANO3038
25.0%
OUTRO119
 
1.0%
FATOR MATERIAL69
 
0.6%
***35
 
0.3%
(Missing)4432
36.4%

Length

2022-05-28T11:32:09.448388image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-28T11:32:09.509396image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
fator7581
49.5%
operacional4474
29.2%
humano3038
19.8%
outro119
 
0.8%
material69
 
0.5%
35
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

recomendacao_conteudo
Categorical

HIGH CARDINALITY
MISSING

Distinct1171
Distinct (%)17.4%
Missing5424
Missing (%)44.6%
Memory size190.1 KiB
Atuar junto à EJ Escola de Aeronáutica Ltda. ME e ao Aeroclube de Itápolis, a fim de que, conjuntamente, estas instituições realizem uma análise de risco sobre a realização de voos de instrução com aproximações de 180 e 360 graus concomitante a tráfegos executando circuitos normais (perna base e reta final) e “IFR simulados”, de maneira a facilitar a identificação dos perigos e a implementação de medidas mitigadoras adequadas.
 
32
Divulgar o conteúdo do presente relatório durante a realização de seminários, palestras e atividades afins voltadas aos proprietários, operadores e exploradores de aeronaves agrícolas.
 
28
AVALIAR A VIABILIDADE DA IMPLANTAçãO DE UM RADAR DE MOVIMENTAçãO DE SUPERFíCIE (SURFACE MOVEMENT RADAR - SMR) EM SBBR, A FIM DE MITIGAR OS RISCOS DE INCURSãO EM PISTA DECORRENTES DOS DIVERSOS PONTOS CEGOS EXISTENTES NO AERóDROMO.
 
26
Atuar junto à LATAM AIRLINES GROUP S.A., a fim de que aquele operador reavalie a adequabilidade do programa de treinamento aplicado a seus pilotos, sobretudo no que diz respeito à frequência e ao controle dos treinamentos de pouso sem o Auto Thrust - Controle Automático de Empuxo (A/THR).
 
26
Atuar junto a INFRAMERICA, a fim de que aquele Operador de Aeródromo adote medidas em relação à estrutura de iluminação dos pátios de estacionamento (Píer Norte e Píer Sul), de modo a evitar que a luz proveniente dos respectivos holofotes interfira negativamente na linha de visada dos controladores da TWR-BR.
 
26
Other values (1166)
6605 

Length

Max length705
Median length270
Mean length277.2902269
Min length68

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique165 ?
Unique (%)2.4%

Sample

1st rowAtuar junto às empresas que operam segundo o RBAC 121 de forma tal que assegure que o treinamento de Corporate Ressource Management (CRM) esteja adequado a realidade daquela empresa, que seja constantemente avaliado e reforçado (com a participação da alta direção), envolva todos os profissionais da instituição, que garanta a integração dos diferentes setores da empresa (corporate), e que, acima de tudo, faça parte da cultura de segurança de voo da organização.
2nd rowAtuar junto à Administração do Aeroporto Internacional de Guarulhos, de forma que este passe a ministrar treinamento teórico e prático de atendimento às vítimas de acidentes envolvendo os principais tipos de aeronaves que operam naquela localidade, principalmente os das Linhas Aéreas Regulares, com especial ênfase ao “Layout” e aos meios de remoção de passageiros do interior destas aeronaves.
3rd rowOrientar as suas organizações subordinadas em relação ao fiel cumprimento do estabelecido na ICA 100-37, de 28ABR2014, no seu item 5.9.3 e na MCA 100-16, de 18NOV2013, no item 2.3.3.
4th rowAtuar junto às empresas que operam segundo o RBAC 121 de forma tal que assegure que o treinamento de Corporate Ressource Management (CRM) esteja adequado a realidade daquela empresa, que seja constantemente avaliado e reforçado (com a participação da alta direção), envolva todos os profissionais da instituição, que garanta a integração dos diferentes setores da empresa (corporate), e que, acima de tudo, faça parte da cultura de segurança de voo da organização.
5th rowAtuar junto à Administração do Aeroporto Internacional de Guarulhos, de forma que este passe a ministrar treinamento teórico e prático de atendimento às vítimas de acidentes envolvendo os principais tipos de aeronaves que operam naquela localidade, principalmente os das Linhas Aéreas Regulares, com especial ênfase ao “Layout” e aos meios de remoção de passageiros do interior destas aeronaves.

Common Values

ValueCountFrequency (%)
Atuar junto à EJ Escola de Aeronáutica Ltda. ME e ao Aeroclube de Itápolis, a fim de que, conjuntamente, estas instituições realizem uma análise de risco sobre a realização de voos de instrução com aproximações de 180 e 360 graus concomitante a tráfegos executando circuitos normais (perna base e reta final) e “IFR simulados”, de maneira a facilitar a identificação dos perigos e a implementação de medidas mitigadoras adequadas.32
 
0.3%
Divulgar o conteúdo do presente relatório durante a realização de seminários, palestras e atividades afins voltadas aos proprietários, operadores e exploradores de aeronaves agrícolas.28
 
0.2%
AVALIAR A VIABILIDADE DA IMPLANTAçãO DE UM RADAR DE MOVIMENTAçãO DE SUPERFíCIE (SURFACE MOVEMENT RADAR - SMR) EM SBBR, A FIM DE MITIGAR OS RISCOS DE INCURSãO EM PISTA DECORRENTES DOS DIVERSOS PONTOS CEGOS EXISTENTES NO AERóDROMO.26
 
0.2%
Atuar junto à LATAM AIRLINES GROUP S.A., a fim de que aquele operador reavalie a adequabilidade do programa de treinamento aplicado a seus pilotos, sobretudo no que diz respeito à frequência e ao controle dos treinamentos de pouso sem o Auto Thrust - Controle Automático de Empuxo (A/THR).26
 
0.2%
Atuar junto a INFRAMERICA, a fim de que aquele Operador de Aeródromo adote medidas em relação à estrutura de iluminação dos pátios de estacionamento (Píer Norte e Píer Sul), de modo a evitar que a luz proveniente dos respectivos holofotes interfira negativamente na linha de visada dos controladores da TWR-BR.26
 
0.2%
ATUAR EM CONJUNTO COM A INFRAMERICA E O DECEA, A FIM DE QUE SEJA AVALIADA A PERTINêNCIA DA INCLUSãO DE HOT SPOTS NA CARTA DE AERóDROMO DE SBBR, VISANDO ALERTAR OS PILOTOS QUE OPERAM NAQUELE AERóDROMO QUANTO à EXISTêNCIA DE PONTOS CEGOS NA áREA DE MANOBRAS.26
 
0.2%
ATUAR EM CONJUNTO COM A INFRAMERICA E O DECEA, A FIM DE QUE CâMERAS DE USO EXCLUSIVO DO DTCEA-BR SEJAM INSTALADAS NO AERóDROMO INTERNACIONAL DE BRASíLIA, DE MODO A GARANTIR A VISUALIZAçãO E O CONTROLE DE TODOS OS PONTOS CEGOS DAQUELE AERóDROMO.26
 
0.2%
ALERTAR OS CONTROLADORES DE TRáFEGO AéREO BRASILEIROS SOBRE A IMPORTâNCIA DA UTILIZAçãO DA FRASEOLOGIA PADRãO PREVISTA NO MCA 100-16, NOTADAMENTE NO QUE SE REFERE O ITEM 3.4.3.4 (INSTRUçõES APóS O POUSO), ONDE é APRESENTADO UM EXEMPLO DE MENSAGEM CORRETA.26
 
0.2%
ANALISAR A PERTINêNCIA DE SE ESTABELECER, COM CLAREZA E EM NORMA, O MOMENTO OU A POSIçãO EM QUE, APóS O POUSO, A TRIPULAçãO DE UMA AERONAVE DEVE TROCAR A FREQUêNCIA DA TORRE DE CONTROLE PARA O CONTROLE DE SOLO.26
 
0.2%
ATUAR EM CONJUNTO COM A INFRAMERICA E O DECEA, A FIM DE QUE SEJA AVALIADA A PERTINêNCIA DE SE MODIFICAR UMA OU AMBAS AS LETRAS DESIGNATIVAS DAS TAXIWAYS “C” (CHARLIE) E “G” (GOLF) DO AERóDROMO INTERNACIONAL DE BRASíLIA, DE MODO A EVITAR CONFUSõES OU EQUíVOCOS POR PARTE DAS TRIPULAçõES QUE OPERAM NAQUELE AERóDROMO.26
 
0.2%
Other values (1161)6475
53.2%
(Missing)5424
44.6%

Length

2022-05-28T11:32:09.622484image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de26872
 
9.5%
a11681
 
4.1%
e7223
 
2.5%
que6493
 
2.3%
da5293
 
1.9%
do5026
 
1.8%
o4249
 
1.5%
os4222
 
1.5%
no3489
 
1.2%
à3374
 
1.2%
Other values (4830)205336
72.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

recomendacao_status
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.1%
Missing5422
Missing (%)44.6%
Memory size190.1 KiB
CUMPRIDA
4474 
AGUARDANDO RESPOSTA
1381 
CUMPRIDA DE FORMA ALTERNATIVA
469 
NÃO CUMPRIDA
 
393
***
 
28

Length

Max length29
Median length8
Mean length11.92468495
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCUMPRIDA
2nd rowCUMPRIDA
3rd rowAGUARDANDO RESPOSTA
4th rowCUMPRIDA
5th rowCUMPRIDA

Common Values

ValueCountFrequency (%)
CUMPRIDA4474
36.8%
AGUARDANDO RESPOSTA1381
 
11.4%
CUMPRIDA DE FORMA ALTERNATIVA469
 
3.9%
NÃO CUMPRIDA393
 
3.2%
***28
 
0.2%
(Missing)5422
44.6%

Length

2022-05-28T11:32:09.727139image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-28T11:32:09.785668image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
cumprida5336
53.8%
aguardando1381
 
13.9%
resposta1381
 
13.9%
de469
 
4.7%
forma469
 
4.7%
alternativa469
 
4.7%
não393
 
4.0%
28
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-05-28T11:32:00.423330image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:31:57.011994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:31:57.673197image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:31:58.374045image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:31:59.082502image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:31:59.777963image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:32:00.524018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:31:57.119130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:31:57.787307image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:31:58.485770image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:31:59.192656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:31:59.882619image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:32:00.636611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:31:57.232714image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:31:57.908302image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:31:58.610311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:31:59.311695image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:31:59.997194image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:32:00.752674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:31:57.349770image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:31:58.031805image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:31:58.735302image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:31:59.433739image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:32:00.109858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:32:00.862291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:31:57.463390image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:31:58.155805image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:31:58.858311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:31:59.555260image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:32:00.220467image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:32:00.962979image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:31:57.571518image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:31:58.264430image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:31:58.971895image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:31:59.668347image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-28T11:32:00.324130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2022-05-28T11:32:09.870980image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-28T11:32:10.014324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-28T11:32:10.156676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-28T11:32:10.320852image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-28T11:32:10.606547image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-28T11:32:01.218913image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-28T11:32:02.970288image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-28T11:32:03.395128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-28T11:32:03.744343image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

ocorrencia_classificacaoocorrencia_cidadeocorrencia_ufocorrencia_aerodromoocorrencia_diaocorrencia_horadivulgacao_relatorio_publicadototal_recomendacoestotal_aeronaves_envolvidasocorrencia_saida_pistaocorrencia_localizacaoocorrencia_tipoocorrencia_tipo_categoriataxonomia_tipo_icaoaeronave_matriculaaeronave_operador_categoriaaeronave_tipo_veiculoaeronave_fabricanteaeronave_modeloaeronave_tipo_icaoaeronave_motor_tipoaeronave_motor_quantidadeaeronave_pmdaeronave_pmd_categoriaaeronave_assentosaeronave_ano_fabricacaoaeronave_registro_categoriaaeronave_registro_segmentoaeronave_voo_origemaeronave_voo_destinoaeronave_fase_operacaoaeronave_tipo_operacaoaeronave_nivel_danoaeronave_fatalidades_totalfator_nomefator_aspectofator_condicionantefator_arearecomendacao_conteudorecomendacao_status
0INCIDENTEPORTO ALEGRE - RSRSSBPA05/01/201220:27:00NÃO01NÃONaNESTOURO DE PNEUFALHA OU MAU FUNCIONAMENTO DE SISTEMA / COMPONENTE | ESTOURO DE PNEUSCF-NPPRCDLPARTICULARAVIÃORAYTHEON AIRCRAFT58BE58PISTÃOBIMOTOR249524956.02003.0AVIÃOPARTICULARFORA DE AERODROMOFORA DE AERODROMOPOUSOPRIVADALEVE0NaNNaNNaNNaNNaNNaN
1ACIDENTEGUARULHOS - SPSPSBGR06/01/201213:44:00SIM31NÃO-23.4355555556 / -46.4730555556COM PESSOAL EM VOOOUTROS | COM PESSOAL EM VOOOTHRPRTKB***AVIÃOAEROSPATIALE AND ALENIAATR-42-500AT45TURBOÉLICEBIMOTOR186001860050.02001.0AVIÃOREGULARFORA DE AERODROMOFORA DE AERODROMODESCIDAREGULARNENHUM0APLICAÇÃO DE COMANDOSDESEMPENHO DO SER HUMANOOPERAÇÃO DA AERONAVEFATOR OPERACIONALAtuar junto às empresas que operam segundo o RBAC 121 de forma tal que assegure que o treinamento de Corporate Ressource Management (CRM) esteja adequado a realidade daquela empresa, que seja constantemente avaliado e reforçado (com a participação da alta direção), envolva todos os profissionais da instituição, que garanta a integração dos diferentes setores da empresa (corporate), e que, acima de tudo, faça parte da cultura de segurança de voo da organização.CUMPRIDA
2ACIDENTEGUARULHOS - SPSPSBGR06/01/201213:44:00SIM31NÃO-23.4355555556 / -46.4730555556COM PESSOAL EM VOOOUTROS | COM PESSOAL EM VOOOTHRPRTKB***AVIÃOAEROSPATIALE AND ALENIAATR-42-500AT45TURBOÉLICEBIMOTOR186001860050.02001.0AVIÃOREGULARFORA DE AERODROMOFORA DE AERODROMODESCIDAREGULARNENHUM0APLICAÇÃO DE COMANDOSDESEMPENHO DO SER HUMANOOPERAÇÃO DA AERONAVEFATOR OPERACIONALAtuar junto à Administração do Aeroporto Internacional de Guarulhos, de forma que este passe a ministrar treinamento teórico e prático de atendimento às vítimas de acidentes envolvendo os principais tipos de aeronaves que operam naquela localidade, principalmente os das Linhas Aéreas Regulares, com especial ênfase ao “Layout” e aos meios de remoção de passageiros do interior destas aeronaves.CUMPRIDA
3ACIDENTEGUARULHOS - SPSPSBGR06/01/201213:44:00SIM31NÃO-23.4355555556 / -46.4730555556COM PESSOAL EM VOOOUTROS | COM PESSOAL EM VOOOTHRPRTKB***AVIÃOAEROSPATIALE AND ALENIAATR-42-500AT45TURBOÉLICEBIMOTOR186001860050.02001.0AVIÃOREGULARFORA DE AERODROMOFORA DE AERODROMODESCIDAREGULARNENHUM0APLICAÇÃO DE COMANDOSDESEMPENHO DO SER HUMANOOPERAÇÃO DA AERONAVEFATOR OPERACIONALOrientar as suas organizações subordinadas em relação ao fiel cumprimento do estabelecido na ICA 100-37, de 28ABR2014, no seu item 5.9.3 e na MCA 100-16, de 18NOV2013, no item 2.3.3.AGUARDANDO RESPOSTA
4ACIDENTEGUARULHOS - SPSPSBGR06/01/201213:44:00SIM31NÃO-23.4355555556 / -46.4730555556COM PESSOAL EM VOOOUTROS | COM PESSOAL EM VOOOTHRPRTKB***AVIÃOAEROSPATIALE AND ALENIAATR-42-500AT45TURBOÉLICEBIMOTOR186001860050.02001.0AVIÃOREGULARFORA DE AERODROMOFORA DE AERODROMODESCIDAREGULARNENHUM0ATENÇÃOASPECTO PSICOLÓGICOINDIVIDUALFATOR HUMANOAtuar junto às empresas que operam segundo o RBAC 121 de forma tal que assegure que o treinamento de Corporate Ressource Management (CRM) esteja adequado a realidade daquela empresa, que seja constantemente avaliado e reforçado (com a participação da alta direção), envolva todos os profissionais da instituição, que garanta a integração dos diferentes setores da empresa (corporate), e que, acima de tudo, faça parte da cultura de segurança de voo da organização.CUMPRIDA
5ACIDENTEGUARULHOS - SPSPSBGR06/01/201213:44:00SIM31NÃO-23.4355555556 / -46.4730555556COM PESSOAL EM VOOOUTROS | COM PESSOAL EM VOOOTHRPRTKB***AVIÃOAEROSPATIALE AND ALENIAATR-42-500AT45TURBOÉLICEBIMOTOR186001860050.02001.0AVIÃOREGULARFORA DE AERODROMOFORA DE AERODROMODESCIDAREGULARNENHUM0ATENÇÃOASPECTO PSICOLÓGICOINDIVIDUALFATOR HUMANOAtuar junto à Administração do Aeroporto Internacional de Guarulhos, de forma que este passe a ministrar treinamento teórico e prático de atendimento às vítimas de acidentes envolvendo os principais tipos de aeronaves que operam naquela localidade, principalmente os das Linhas Aéreas Regulares, com especial ênfase ao “Layout” e aos meios de remoção de passageiros do interior destas aeronaves.CUMPRIDA
6ACIDENTEGUARULHOS - SPSPSBGR06/01/201213:44:00SIM31NÃO-23.4355555556 / -46.4730555556COM PESSOAL EM VOOOUTROS | COM PESSOAL EM VOOOTHRPRTKB***AVIÃOAEROSPATIALE AND ALENIAATR-42-500AT45TURBOÉLICEBIMOTOR186001860050.02001.0AVIÃOREGULARFORA DE AERODROMOFORA DE AERODROMODESCIDAREGULARNENHUM0ATENÇÃOASPECTO PSICOLÓGICOINDIVIDUALFATOR HUMANOOrientar as suas organizações subordinadas em relação ao fiel cumprimento do estabelecido na ICA 100-37, de 28ABR2014, no seu item 5.9.3 e na MCA 100-16, de 18NOV2013, no item 2.3.3.AGUARDANDO RESPOSTA
7ACIDENTEGUARULHOS - SPSPSBGR06/01/201213:44:00SIM31NÃO-23.4355555556 / -46.4730555556COM PESSOAL EM VOOOUTROS | COM PESSOAL EM VOOOTHRPRTKB***AVIÃOAEROSPATIALE AND ALENIAATR-42-500AT45TURBOÉLICEBIMOTOR186001860050.02001.0AVIÃOREGULARFORA DE AERODROMOFORA DE AERODROMODESCIDAREGULARNENHUM0CAPACITAÇÃO E TREINAMENTOASPECTO PSICOLÓGICOORGANIZACIONALFATOR HUMANOAtuar junto às empresas que operam segundo o RBAC 121 de forma tal que assegure que o treinamento de Corporate Ressource Management (CRM) esteja adequado a realidade daquela empresa, que seja constantemente avaliado e reforçado (com a participação da alta direção), envolva todos os profissionais da instituição, que garanta a integração dos diferentes setores da empresa (corporate), e que, acima de tudo, faça parte da cultura de segurança de voo da organização.CUMPRIDA
8ACIDENTEGUARULHOS - SPSPSBGR06/01/201213:44:00SIM31NÃO-23.4355555556 / -46.4730555556COM PESSOAL EM VOOOUTROS | COM PESSOAL EM VOOOTHRPRTKB***AVIÃOAEROSPATIALE AND ALENIAATR-42-500AT45TURBOÉLICEBIMOTOR186001860050.02001.0AVIÃOREGULARFORA DE AERODROMOFORA DE AERODROMODESCIDAREGULARNENHUM0CAPACITAÇÃO E TREINAMENTOASPECTO PSICOLÓGICOORGANIZACIONALFATOR HUMANOAtuar junto à Administração do Aeroporto Internacional de Guarulhos, de forma que este passe a ministrar treinamento teórico e prático de atendimento às vítimas de acidentes envolvendo os principais tipos de aeronaves que operam naquela localidade, principalmente os das Linhas Aéreas Regulares, com especial ênfase ao “Layout” e aos meios de remoção de passageiros do interior destas aeronaves.CUMPRIDA
9ACIDENTEGUARULHOS - SPSPSBGR06/01/201213:44:00SIM31NÃO-23.4355555556 / -46.4730555556COM PESSOAL EM VOOOUTROS | COM PESSOAL EM VOOOTHRPRTKB***AVIÃOAEROSPATIALE AND ALENIAATR-42-500AT45TURBOÉLICEBIMOTOR186001860050.02001.0AVIÃOREGULARFORA DE AERODROMOFORA DE AERODROMODESCIDAREGULARNENHUM0CAPACITAÇÃO E TREINAMENTOASPECTO PSICOLÓGICOORGANIZACIONALFATOR HUMANOOrientar as suas organizações subordinadas em relação ao fiel cumprimento do estabelecido na ICA 100-37, de 28ABR2014, no seu item 5.9.3 e na MCA 100-16, de 18NOV2013, no item 2.3.3.AGUARDANDO RESPOSTA

Last rows

ocorrencia_classificacaoocorrencia_cidadeocorrencia_ufocorrencia_aerodromoocorrencia_diaocorrencia_horadivulgacao_relatorio_publicadototal_recomendacoestotal_aeronaves_envolvidasocorrencia_saida_pistaocorrencia_localizacaoocorrencia_tipoocorrencia_tipo_categoriataxonomia_tipo_icaoaeronave_matriculaaeronave_operador_categoriaaeronave_tipo_veiculoaeronave_fabricanteaeronave_modeloaeronave_tipo_icaoaeronave_motor_tipoaeronave_motor_quantidadeaeronave_pmdaeronave_pmd_categoriaaeronave_assentosaeronave_ano_fabricacaoaeronave_registro_categoriaaeronave_registro_segmentoaeronave_voo_origemaeronave_voo_destinoaeronave_fase_operacaoaeronave_tipo_operacaoaeronave_nivel_danoaeronave_fatalidades_totalfator_nomefator_aspectofator_condicionantefator_arearecomendacao_conteudorecomendacao_status
12157INCIDENTEMANAUS - AMAMSBEG30/12/202114:41:00NÃO01NÃO-3.04111111 / -60.05055556ESTOURO DE PNEUFALHA OU MAU FUNCIONAMENTO DE SISTEMA / COMPONENTE | ESTOURO DE PNEUSCF-NPPTOCV***AVIÃOEMBRAEREMB-110P1E110TURBOÉLICEBIMOTOR5670567021.01981.0AVIÃOTÁXI AÉREOCARAUARIEDUARDO GOMESCORRIDA APÓS POUSOTÁXI AÉREOLEVE0NaNNaNNaNNaNNaNNaN
12158INCIDENTESÃO PAULO - SPSPSBSP30/12/202113:15:00NÃO01SIM-23.626111 / -46.656389COM TREM DE POUSOFALHA OU MAU FUNCIONAMENTO DE SISTEMA / COMPONENTE | COM TREM DE POUSOSCF-NPPPAFP***AVIÃOCESSNA AIRCRAFT208BC208TURBOÉLICEMONOMOTOR396939697.02013.0AVIÃOPARTICULARCONGONHASDOUTOR RAMALHO FRANCOTÁXIPRIVADANENHUM0NaNNaNNaNNaNNaNNaN
12159INCIDENTESÃO PAULO - SPSPSBSP30/12/202113:15:00NÃO01SIM-23.626111 / -46.656389EXCURSÃO DE PISTAEXCURSÃO DE PISTAREPPAFP***AVIÃOCESSNA AIRCRAFT208BC208TURBOÉLICEMONOMOTOR396939697.02013.0AVIÃOPARTICULARCONGONHASDOUTOR RAMALHO FRANCOTÁXIPRIVADANENHUM0NaNNaNNaNNaNNaNNaN
12160ACIDENTEJATAÍ - GOGO####30/12/202120:30:00NÃO01NÃO-17.999194 / -51.642861EXCURSÃO DE PISTAEXCURSÃO DE PISTAREPTWBA***AVIÃOEMBRAEREMB-202AIPANPISTÃOMONOMOTOR180018001.02013.0AVIÃOPARTICULARPISTA DE POUSO EVENTUALPISTA DE POUSO EVENTUALDECOLAGEMAGRÍCOLASUBSTANCIAL0NaNNaNNaNNaNNaNNaN
12161ACIDENTEJATAÍ - GOGO####30/12/202120:30:00NÃO01NÃO-17.999194 / -51.642861PERDA DE CONTROLE NO SOLOPERDA DE CONTROLE NO SOLOLOC-GPTWBA***AVIÃOEMBRAEREMB-202AIPANPISTÃOMONOMOTOR180018001.02013.0AVIÃOPARTICULARPISTA DE POUSO EVENTUALPISTA DE POUSO EVENTUALDECOLAGEMAGRÍCOLASUBSTANCIAL0NaNNaNNaNNaNNaNNaN
12162ACIDENTEMARACAÍ - SPSP*****31/12/202109:30:00NÃO01NÃO-22.585556 / -50.753889OPERAÇÃO A BAIXA ALTITUDEOPERAÇÃO A BAIXA ALTITUDELALTPRVPR***AVIÃOAIR TRACTORAT-502BAT5TTURBOÉLICEMONOMOTOR362936291.02018.0AVIÃOAGRÍCOLA******MANOBRAESPECIALIZADASUBSTANCIAL0NaNNaNNaNNaNNaNNaN
12163ACIDENTEMARACAÍ - SPSP*****31/12/202109:30:00NÃO01NÃO-22.585556 / -50.753889PERDA DE CONTROLE EM VOOPERDA DE CONTROLE EM VOOLOC-IPRVPR***AVIÃOAIR TRACTORAT-502BAT5TTURBOÉLICEMONOMOTOR362936291.02018.0AVIÃOAGRÍCOLA******MANOBRAESPECIALIZADASUBSTANCIAL0NaNNaNNaNNaNNaNNaN
12164INCIDENTE GRAVENOVO HAMBURGO - RSRSSSNH31/12/202111:59:00NÃO01NÃO-29.695833 / -51.081667POUSO BRUSCOCONTATO ANORMAL COM A PISTA | POUSO BRUSCOARCPPFLY***AVIÃOAERO BOEROAB-115AB11PISTÃOMONOMOTOR7707702.01990.0AVIÃOINSTRUÇÃONOVO HAMBURGONOVO HAMBURGOPOUSOINSTRUÇÃOLEVE0NaNNaNNaNNaNNaNNaN
12165INCIDENTECURITIBA - PRPRSBBI31/12/202115:12:00NÃO01NÃO-25.403333 / -49.233611COLISÃO COM OBSTÁCULOS NO SOLOCOLISÃO NO SOLO | COLISÃO COM OBSTÁCULOS NO SOLOGCOLPTWSA***AVIÃOBEECH AIRCRAFT58BE58PISTÃOBIMOTOR244924496.01972.0AVIÃOADMINISTRAÇÃO DIRETASANTOS DUMONTBACACHERITÁXIPOLICIALLEVE0NaNNaNNaNNaNNaNNaN
12166INCIDENTEPETROLINA - PEPESBPL31/12/202120:30:00NÃO01NÃO-9.3675 / -40.56361111111FALHA DO MOTOR EM VOOFALHA OU MAU FUNCIONAMENTO DO MOTOR | FALHA DO MOTOR EM VOOSCF-PPPRGXM***AVIÃOBOEING COMPANY737-8EHB738JATOBIMOTOR7053370533199.02013.0AVIÃOREGULARORLANDO BEZERRA DE MENEZESGOVERNADOR ANDRÉ FRANCO MONTOROSUBIDAREGULARLEVE0NaNNaNNaNNaNNaNNaN

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ocorrencia_classificacaoocorrencia_cidadeocorrencia_ufocorrencia_aerodromoocorrencia_diaocorrencia_horadivulgacao_relatorio_publicadototal_recomendacoestotal_aeronaves_envolvidasocorrencia_saida_pistaocorrencia_localizacaoocorrencia_tipoocorrencia_tipo_categoriataxonomia_tipo_icaoaeronave_matriculaaeronave_operador_categoriaaeronave_tipo_veiculoaeronave_fabricanteaeronave_modeloaeronave_tipo_icaoaeronave_motor_tipoaeronave_motor_quantidadeaeronave_pmdaeronave_pmd_categoriaaeronave_assentosaeronave_ano_fabricacaoaeronave_registro_categoriaaeronave_registro_segmentoaeronave_voo_origemaeronave_voo_destinoaeronave_fase_operacaoaeronave_tipo_operacaoaeronave_nivel_danoaeronave_fatalidades_totalfator_nomefator_aspectofator_condicionantefator_arearecomendacao_conteudorecomendacao_status# duplicates
0ACIDENTEITÁPOLIS - SPSPSDIO12/07/201810:50:00SIM62NÃO-215.997.222.22 / -488.327.777.77TRÁFEGO AÉREOPERDA DE SEPARAÇÃO / COLISÃO EM VOO | TRÁFEGO AÉREOMACPREJK***AVIÃOCESSNA AIRCRAFT152C152PISTÃOMONOMOTOR7577572.01979.0AVIÃOINSTRUÇÃOAEROCLUBE DE ITÁPOLISAEROCLUBE DE ITÁPOLISPOUSOINSTRUÇÃOSUBSTANCIAL0APLICAÇÃO DE COMANDOSDESEMPENHO DO SER HUMANOOPERAÇÃO DA AERONAVEFATOR OPERACIONALAtuar junto à EJ Escola de Aeronáutica Ltda. ME e ao Aeroclube de Itápolis, a fim de que, conjuntamente, estas instituições realizem uma análise de risco sobre a realização de voos de instrução com aproximações de 180 e 360 graus concomitante a tráfegos executando circuitos normais (perna base e reta final) e “IFR simulados”, de maneira a facilitar a identificação dos perigos e a implementação de medidas mitigadoras adequadas.CUMPRIDA2
1ACIDENTEITÁPOLIS - SPSPSDIO12/07/201810:50:00SIM62NÃO-215.997.222.22 / -488.327.777.77TRÁFEGO AÉREOPERDA DE SEPARAÇÃO / COLISÃO EM VOO | TRÁFEGO AÉREOMACPREJK***AVIÃOCESSNA AIRCRAFT152C152PISTÃOMONOMOTOR7577572.01979.0AVIÃOINSTRUÇÃOAEROCLUBE DE ITÁPOLISAEROCLUBE DE ITÁPOLISPOUSOINSTRUÇÃOSUBSTANCIAL0CULTURA ORGANIZACIONALASPECTO PSICOLÓGICOORGANIZACIONALFATOR HUMANOAtuar junto à EJ Escola de Aeronáutica Ltda. ME e ao Aeroclube de Itápolis, a fim de que, conjuntamente, estas instituições realizem uma análise de risco sobre a realização de voos de instrução com aproximações de 180 e 360 graus concomitante a tráfegos executando circuitos normais (perna base e reta final) e “IFR simulados”, de maneira a facilitar a identificação dos perigos e a implementação de medidas mitigadoras adequadas.CUMPRIDA2
2ACIDENTEITÁPOLIS - SPSPSDIO12/07/201810:50:00SIM62NÃO-215.997.222.22 / -488.327.777.77TRÁFEGO AÉREOPERDA DE SEPARAÇÃO / COLISÃO EM VOO | TRÁFEGO AÉREOMACPREJK***AVIÃOCESSNA AIRCRAFT152C152PISTÃOMONOMOTOR7577572.01979.0AVIÃOINSTRUÇÃOAEROCLUBE DE ITÁPOLISAEROCLUBE DE ITÁPOLISPOUSOINSTRUÇÃOSUBSTANCIAL0FRASEOLOGIA DA TRIPULAÇÃODESEMPENHO DO SER HUMANOOPERAÇÃO DA AERONAVEFATOR OPERACIONALAtuar junto à EJ Escola de Aeronáutica Ltda. ME e ao Aeroclube de Itápolis, a fim de que, conjuntamente, estas instituições realizem uma análise de risco sobre a realização de voos de instrução com aproximações de 180 e 360 graus concomitante a tráfegos executando circuitos normais (perna base e reta final) e “IFR simulados”, de maneira a facilitar a identificação dos perigos e a implementação de medidas mitigadoras adequadas.CUMPRIDA2
3ACIDENTEITÁPOLIS - SPSPSDIO12/07/201810:50:00SIM62NÃO-215.997.222.22 / -488.327.777.77TRÁFEGO AÉREOPERDA DE SEPARAÇÃO / COLISÃO EM VOO | TRÁFEGO AÉREOMACPREJK***AVIÃOCESSNA AIRCRAFT152C152PISTÃOMONOMOTOR7577572.01979.0AVIÃOINSTRUÇÃOAEROCLUBE DE ITÁPOLISAEROCLUBE DE ITÁPOLISPOUSOINSTRUÇÃOSUBSTANCIAL0INDISCIPLINA DE VOODESEMPENHO DO SER HUMANOOPERAÇÃO DA AERONAVEFATOR OPERACIONALAtuar junto à EJ Escola de Aeronáutica Ltda. ME e ao Aeroclube de Itápolis, a fim de que, conjuntamente, estas instituições realizem uma análise de risco sobre a realização de voos de instrução com aproximações de 180 e 360 graus concomitante a tráfegos executando circuitos normais (perna base e reta final) e “IFR simulados”, de maneira a facilitar a identificação dos perigos e a implementação de medidas mitigadoras adequadas.CUMPRIDA2
4ACIDENTEITÁPOLIS - SPSPSDIO12/07/201810:50:00SIM62NÃO-215.997.222.22 / -488.327.777.77TRÁFEGO AÉREOPERDA DE SEPARAÇÃO / COLISÃO EM VOO | TRÁFEGO AÉREOMACPREJK***AVIÃOCESSNA AIRCRAFT152C152PISTÃOMONOMOTOR7577572.01979.0AVIÃOINSTRUÇÃOAEROCLUBE DE ITÁPOLISAEROCLUBE DE ITÁPOLISPOUSOINSTRUÇÃOSUBSTANCIAL0JULGAMENTO DE PILOTAGEMDESEMPENHO DO SER HUMANOOPERAÇÃO DA AERONAVEFATOR OPERACIONALAtuar junto à EJ Escola de Aeronáutica Ltda. ME e ao Aeroclube de Itápolis, a fim de que, conjuntamente, estas instituições realizem uma análise de risco sobre a realização de voos de instrução com aproximações de 180 e 360 graus concomitante a tráfegos executando circuitos normais (perna base e reta final) e “IFR simulados”, de maneira a facilitar a identificação dos perigos e a implementação de medidas mitigadoras adequadas.CUMPRIDA2
5ACIDENTEITÁPOLIS - SPSPSDIO12/07/201810:50:00SIM62NÃO-215.997.222.22 / -488.327.777.77TRÁFEGO AÉREOPERDA DE SEPARAÇÃO / COLISÃO EM VOO | TRÁFEGO AÉREOMACPREJK***AVIÃOCESSNA AIRCRAFT152C152PISTÃOMONOMOTOR7577572.01979.0AVIÃOINSTRUÇÃOAEROCLUBE DE ITÁPOLISAEROCLUBE DE ITÁPOLISPOUSOINSTRUÇÃOSUBSTANCIAL0PERCEPÇÃOASPECTO PSICOLÓGICOINDIVIDUALFATOR HUMANOAtuar junto à EJ Escola de Aeronáutica Ltda. ME e ao Aeroclube de Itápolis, a fim de que, conjuntamente, estas instituições realizem uma análise de risco sobre a realização de voos de instrução com aproximações de 180 e 360 graus concomitante a tráfegos executando circuitos normais (perna base e reta final) e “IFR simulados”, de maneira a facilitar a identificação dos perigos e a implementação de medidas mitigadoras adequadas.CUMPRIDA2
6ACIDENTEITÁPOLIS - SPSPSDIO12/07/201810:50:00SIM62NÃO-215.997.222.22 / -488.327.777.77TRÁFEGO AÉREOPERDA DE SEPARAÇÃO / COLISÃO EM VOO | TRÁFEGO AÉREOMACPREJK***AVIÃOCESSNA AIRCRAFT152C152PISTÃOMONOMOTOR7577572.01979.0AVIÃOINSTRUÇÃOAEROCLUBE DE ITÁPOLISAEROCLUBE DE ITÁPOLISPOUSOINSTRUÇÃOSUBSTANCIAL0POUCA EXPERIÊNCIA DO PILOTODESEMPENHO DO SER HUMANOOPERAÇÃO DA AERONAVEFATOR OPERACIONALAtuar junto à EJ Escola de Aeronáutica Ltda. ME e ao Aeroclube de Itápolis, a fim de que, conjuntamente, estas instituições realizem uma análise de risco sobre a realização de voos de instrução com aproximações de 180 e 360 graus concomitante a tráfegos executando circuitos normais (perna base e reta final) e “IFR simulados”, de maneira a facilitar a identificação dos perigos e a implementação de medidas mitigadoras adequadas.CUMPRIDA2
7ACIDENTEITÁPOLIS - SPSPSDIO12/07/201810:50:00SIM62NÃO-215.997.222.22 / -488.327.777.77TRÁFEGO AÉREOPERDA DE SEPARAÇÃO / COLISÃO EM VOO | TRÁFEGO AÉREOMACPREJK***AVIÃOCESSNA AIRCRAFT152C152PISTÃOMONOMOTOR7577572.01979.0AVIÃOINSTRUÇÃOAEROCLUBE DE ITÁPOLISAEROCLUBE DE ITÁPOLISPOUSOINSTRUÇÃOSUBSTANCIAL0SUPERVISÃO GERENCIALDESEMPENHO DO SER HUMANOOPERAÇÃO DA AERONAVEFATOR OPERACIONALAtuar junto à EJ Escola de Aeronáutica Ltda. ME e ao Aeroclube de Itápolis, a fim de que, conjuntamente, estas instituições realizem uma análise de risco sobre a realização de voos de instrução com aproximações de 180 e 360 graus concomitante a tráfegos executando circuitos normais (perna base e reta final) e “IFR simulados”, de maneira a facilitar a identificação dos perigos e a implementação de medidas mitigadoras adequadas.CUMPRIDA2
8ACIDENTEITÁPOLIS - SPSPSDIO12/07/201810:50:00SIM62NÃO-215.997.222.22 / -488.327.777.77TRÁFEGO AÉREOPERDA DE SEPARAÇÃO / COLISÃO EM VOO | TRÁFEGO AÉREOMACPRJEA***AVIÃOCESSNA AIRCRAFT150MC150PISTÃOMONOMOTOR7267262.01975.0AVIÃOINSTRUÇÃOAEROCLUBE DE ITÁPOLISAEROCLUBE DE ITÁPOLISPOUSOINSTRUÇÃOSUBSTANCIAL0APLICAÇÃO DE COMANDOSDESEMPENHO DO SER HUMANOOPERAÇÃO DA AERONAVEFATOR OPERACIONALAtuar junto à EJ Escola de Aeronáutica Ltda. ME e ao Aeroclube de Itápolis, a fim de que, conjuntamente, estas instituições realizem uma análise de risco sobre a realização de voos de instrução com aproximações de 180 e 360 graus concomitante a tráfegos executando circuitos normais (perna base e reta final) e “IFR simulados”, de maneira a facilitar a identificação dos perigos e a implementação de medidas mitigadoras adequadas.CUMPRIDA2
9ACIDENTEITÁPOLIS - SPSPSDIO12/07/201810:50:00SIM62NÃO-215.997.222.22 / -488.327.777.77TRÁFEGO AÉREOPERDA DE SEPARAÇÃO / COLISÃO EM VOO | TRÁFEGO AÉREOMACPRJEA***AVIÃOCESSNA AIRCRAFT150MC150PISTÃOMONOMOTOR7267262.01975.0AVIÃOINSTRUÇÃOAEROCLUBE DE ITÁPOLISAEROCLUBE DE ITÁPOLISPOUSOINSTRUÇÃOSUBSTANCIAL0CULTURA ORGANIZACIONALASPECTO PSICOLÓGICOORGANIZACIONALFATOR HUMANOAtuar junto à EJ Escola de Aeronáutica Ltda. ME e ao Aeroclube de Itápolis, a fim de que, conjuntamente, estas instituições realizem uma análise de risco sobre a realização de voos de instrução com aproximações de 180 e 360 graus concomitante a tráfegos executando circuitos normais (perna base e reta final) e “IFR simulados”, de maneira a facilitar a identificação dos perigos e a implementação de medidas mitigadoras adequadas.CUMPRIDA2